Identification of arthropathy and myopathy of the temporomandibular syndrome by biomechanical facial features.

BACKGROUND: Temporomandibular disorders (TMDs) are pathological conditions affecting the temporomandibular joint and/or masticatory muscles. The current diagnosis of TMDs is complex and multi-factorial, including questionnaires, medical testing and the use of diagnostic methods, such as computed tomography and magnetic resonance imaging. The evaluation, like the mandibular range of motion, needs the experience of the professional in the field and as such, there is a probability of human error when diagnosing TMD. The aim of this study is therefore to develop a method with infrared cameras, using the maximum range of motion of the jaw and four types of classifiers to help professionals to classify the pathologies of the temporomandibular joint (TMJ) and related muscles in a quantitative way, thus helping to diagnose and follow up on TMD. METHODS: Forty individuals were evaluated and diagnosed using the diagnostic criteria for temporomandibular disorders (DC/TMD) scale, and divided into three groups: 20 healthy individuals (control group CG), 10 individuals with myopathies (MG), 10 individuals with arthropathies (AG). A quantitative assessment was carried out by motion capture. The TMJ movement was captured with camera tracking markers mounted on the face and jaw of each individual. Data was exported and analyzed using a custom-made software. The data was used to identify and place each participant into one of three classes using the K-nearest neighbor (KNN), Random Forest, Naïve Bayes and Support Vector Machine algorithms. RESULTS: Significant precision and accuracy (over 90%) was reached by KNN when classifying the three groups. The other methods tested presented lower values of sensitivity and specificity. CONCLUSION: The quantitative TMD classification method proposed herein has significant precision and accuracy over the DC/TMD standards. However, this should not be used as a standalone tool but as an auxiliary method for diagnostic TMDs.

Kinematic and electromyographic analysis of school children gait with and without load in the backpack

AbstractIntroduction: Excessive load on the backpacks can lead to musculoskeletal injuries and gait alterations. The objective of this study was to analyze the electromyographic (EMG) activity in association to the kinematic during the stance and balance phases of gait performed with and without the use of the backpack. Methods Twelve volunteers have executed a gait cycle in 3 tasks: without the school backpack (SM), with a backpack with load equivalent to 10% (M10) and 20% (M20) of the body weight (BW). It was evaluated the ankle, knee and hip angular excursion (AE), linear displacement (LD) of the toe and ankle, knee, hip, shoulder, head and EMG activity of the tibialis anterior (TA), vastus medialis (VM), rectus abdominis (RA), gastrocnemius lateral (GL), biceps femoris (BF) and spinal erector (EE) muscles. Results In the LD in the stance phase there was an AP increase for the toe and ankle, hip and head (p<0.043). In the balance phase for VT (vertical) direction, the shoulder presented a smaller displacement as well as the toe, ankle, knee, hip, shoulder and head in the ML direction. In the AP direction, the toe displacement was smaller as well as the ankle (p < 0.038). The AE in both phases was smaller for the hip (p <0.006). In the balance phase the IEMG was higher for the RA (p = 0.034). Conclusion These results suggest that the transport of school backpacks with loads of more than 10%BW causes changes in the kinematic and in the muscular recruitment pattern.

A specialized motion capture system for real-time analysis of mandibular movements using infrared cameras.

BACKGROUND: In the last years, several methods and devices have been proposed to record the human mandibular movements, since they provide quantitative parameters that support the diagnosis and treatment of temporomandibular disorders. The techniques currently employed suffer from a number of drawbacks including high price, unnatural to use, lack of support for real-time analysis and mandibular movements recording as a pure rotation. In this paper, we propose a specialized optical motion capture system, which causes a minimum obstruction and can support 3D mandibular movement analysis in real-time. METHODS: We used three infrared cameras together with nine reflective markers that were placed at key points of the face. Some classical techniques are suggested to conduct the camera calibration and three-dimensional reconstruction and we propose some specialized algorithms to automatically recognize our set of markers and track them along a motion capture session. RESULTS: To test the system, we developed a prototype software and performed a clinical experiment in a group of 22 subjects. They were instructed to execute several movements for the functional evaluation of the mandible while the system was employed to record them. The acquired parameters and the reconstructed trajectories were used to confirm the typical function of temporomandibular joint in some subjects and to highlight its abnormal behavior in others. CONCLUSIONS: The proposed system is an alternative to the existing optical, mechanical, electromagnetic and ultrasonic-based methods, and intends to address some drawbacks of currently available solutions. Its main goal is to assist specialists in diagnostic and treatment of temporomandibular disorders, since simple visual inspection may not be sufficient for a precise assessment of temporomandibular joint and associated muscles.